A Study to Analyse Covid-19 Outbreak Using Multiple Linear Regression: A Supervised Machine Learning Approach

Introduction: Globally, COVID-19 have impacted people's quality of life. Machine learning have recently be-come popular for making predictions because of their precision and adaptability in identifying diseases. This study aims to identify significant predictors for daily active cases and to visualise trends in daily active, posi-tive cases, and immunisations. Material and methods: This paper utilized secondary data from Covid-19 health bulletin of Uttarakhand and multiple linear regression as a part of supervised machine learning is performed to analyse dataset. Results: Multiple Linear Regression model is more accurate in terms of greater score of R2 (=0.90)as com-pared to Linear Regression model with R2=0.88. The daily number of positive, cured, deceased cases are signif-icant predictors for daily active cases (p <0.001). Using time series linear regression approach, cumulative number of active cases is forecasted to be 6695 (95% CI: 6259 - 7131) on 93rd day since 18 Sep 2022, if simi-lar trend continues in upcoming 3 weeks in Uttarakhand. Conclusion: Regression models are useful for forecasting COVID-19 instances, which will help governments and health organisations to address this pandemic in future and establish appropriate policies and recom-mendations for regular prevention.

Analysis of factors influencing the medical insurance balance of hospitalization expenses for gastric cancer surgery patients under DRG payment / 中华医院管理杂志

Objective:To analyze the influencing factors of the medical insurance balance of hospitalization expenses for gastric cancer surgery patients under DRG payment, for reference for promoting the reform of DRG payment in public hospitals and controlling hospitalization expenses reasonably.Methods:The gastric cancer patients enrolled in the gastroenterology department of a tertiary comprehensive hospital from January to July 2022 were selected as the research subjects. The indicators such as patient age, medical insurance balance, hospitalization expenses and their composition were extracted from the hospital information management system and the medical insurance settlement system a certain city. Descriptive analysis was conducted for all data, and stepwise multiple linear regression was used to analyze the influencing factors of patients′ medical insurance balance. Monte Carlo simulation method was used to simulate different combination scenarios of various influencing factors to analyze the probability of medical insurance balance.Results:A total of 205 patients were contained, including 117 in the medical insurance balance group and 88 in the loss group. The difference in hospitalization expenses and medical insurance balance between the two groups of patients were statistically significant ( P<0.05). The intervention of medical insurance specialists, correct DRG enrollment, parenteral nutrition preparation costs, anti infective drug costs, examination costs, and consumables costs were the influencing factors of patient medical insurance balance ( P<0.05). Through Monte Carlo simulation verification, patients with different cost parenteral nutrition preparations, or different anti infective drug schemes had the higher probability of medical insurance balance in the scenario where the medical insurance commissioner intervenes and the DRG enrollment was correct. Conclusions:The hospital adopted interventions from medical insurance specialists to ensure the correct DRG enrollment of patients, accurate use of parenteral nutrition and anti infective drugs, and reasonable control the cost of examinations and consumables, which could increase the probability of medical insurance balance for gastric cancer surgery patients. In the future, hospitals should further promote the procurement of drug consumables in bulk, reduce unnecessary examinations, develop standardized perioperative nutritional interventions and anti infection treatment pathways, ensure the accuracy of DRG enrollment, optimize clinical diagnosis and treatment pathways to improve the efficiency of medical insurance fund utilization and provide high-quality medical services for patients.

Impacto das mudanças climáticas e da qualidade do ar em hospitalizações por doenças respiratórias em municípios da Região Metropolitana de São Paulo (RMSP), Brasil / Impact of climate change and air quality on hospitalizations for respiratory diseases in municipalities in the Metropolitan Region of São Paulo (MRSP), Brazil

Resumo Este estudo teve por objetivo analisar os possíveis impactos das mudanças climáticas na saúde respiratória nos municípios de Santo André e São Caetano do Sul. Foram analisados dados meteorológicos históricos (temperatura, precipitação, umidade relativa e pressão atmosférica), de qualidade do ar (concentrações de MP10 e O3) e de saúde respiratória (taxas de incidência de internações por doenças respiratórias - TIIDR), relacionados através de modelos estatísticos de Regressão Linear Múltipla (RLM). Dados meteorológicos de projeções climáticas futuras (2019-2099) de três modelos climáticos (um global e dois regionalizados) em dois cenários de emissão foram aplicados aos modelos de RLM. Os resultados das projeções mostraram um aumento de até 10% nas TIIDR em relação aos níveis atuais para São Caetano do Sul no período de 2070-2099. Em Santo André as projeções indicaram redução de até 26% nas TIIDR. A variável de maior peso nos modelos de RLM de Santo André foi a temperatura (-2,15x) indicando que o aquecimento é inversamente proporcional ao aumento nas TIIDR, enquanto em São Caetano do Sul a pressão atmosférica teve o maior peso (2,44x). Para próximos trabalhos recomenda-se a inclusão de projeções futuras de concentrações de poluentes atmosféricos.

Abstract The scope of this study was to analyze the possible impacts of climate change on respiratory health in the municipalities of Santo André and São Caetano do Sul. Historical meteorological data (temperature, precipitation, relative humidity and atmospheric pressure), air quality data (concentrations of PM10 and O3) and respiratory health data (incidence rates of hospitalizations for respiratory diseases - IRHRD) were related through statistical models of Multiple Linear Regression (MLR). Meteorological data from future climate projections (2019-2099) from three different climate models (one global and two regionalized) in two emission scenarios were applied to the MLR models. The results showed that the IRHRD will suffer an increase of up to 10% in relation to the current levels for São Caetano do Sul in the 2070-2099 period. In Santo André, projections indicated a reduction of up to 26% in IRHRD. The most important variable in the MLR models for Santo André was temperature (-2,15x), indicating an inverse relationship between global warming and an increase in IRHRD, while in São Caetano the atmospheric pressure had the greatest weight (2.44x). For future studies, the inclusion of future projections of PM10 concentrations is recommended.

Reasonably conduct the multiple linear regression analysis combined with the propensity score analysis / 四川精神卫生

The purpose of this paper was to introduce how to combine the propensity score analysis to reasonably carry out multiple linear regression analysis. Firstly， it introduced 3 basic concepts related to the propensity score analysis. Secondly， it presented the core contents of the propensity score analysis， that was， three matching methods. Thirdly， through an epidemiological survey example， it gave the whole process of how to use SAS software for the analysis. The contents were as follows： ① for the original data set， test whether the difference of covariates between the treatment group and the control group was statistically significant； ② directly implement the multiple linear regression analysis for the original data set； ③ the propensity score analysis was used to generate the matched data set； ④ for the matched data set， test whether the difference of covariates between the treatment group and the control group was statistically significant； ⑤ a reasonable multiple linear regression analysis was used for the matched data set.

Influence of built environment on circulatory disease mortality: A case study of 17 cities in China / 环境与职业医学

Background In view of circulatory diseases, most previous studies focused on the impacts of air pollution and meteorological factors, while ignoring the influence of built environment. Objective To investigate and quantify the impact of built environment on circulatory diseases in China. Methods Circulatory disease mortality data and built environment data (including urban greenery coverage, urban land use, urban land use mix, urban road facilities and urban medical facilities) of 17 cities in China from 2000 to 2019 were collected. Multiple linear regression was used to analyze which built environment elements had significant influence on circulatory diseases, and to quantify their effects. Furthermore, the changes of built environment indicators on circulatory disease mortality were evaluated under different levels of urban economic development and various air quality. Results The built environment affected the mortality of circulatory diseases during the study period (P<0.05). Urban green space and commercial land area were negatively correlated with circulatory disease mortality, and regression coefficients were −0.550 and −0.280, respectively (P<0.05). On the contrary, the increase of urban road area, residential land ratio, and the degree of land use mix were positively associated with circulatory disease mortality, and their regression coefficients were 0.322, 0.283, and 0.176, respectively (P<0.05). When the level of urban economic development was low, the impact of commercial land use ratio on circulatory diseases was stronger, and the regression coefficient was −0.476 (P<0.05). When urban air pollution worsened, the impacts of per capita green coverage area and per capita urban road area on the disease were more prominent, and the regression coefficients were −0.528 and 0.372, respectively (P<0.05). Conclusion There is a significant correlation between urban built environment and mortality of circulatory diseases. To be specific, circulatory disease mortality has a negative correlation with per capita green coverage area and commercial land use ratio, and a positive correlation with per capita urban road area, residential land ratio and degree of land use mix.

Indicadores demográficos en la incidencia de la covid-19 en Santiago de Cuba / Indicadores demográficos na incidência da Covid-19 em Santiago de Cuba / Demographic indicators in the incidence of Covid-19 in Santiago de Cuba

Los indicadores demográficos han sido empleados por algunos investigadores para estimar el número de personas infectadas por la covid-19. El presente trabajo tiene como primer objetivo determinar en qué medida la incidencia de casos con covid-19 en los municipios de la provincia de Santiago de Cuba puede ser explicada a partir de determinados indicadores demográficos. El segundo objetivo es construir una jerarquía de grupos de municipios de acuerdo al comportamiento diferenciado de los indicadores demográficos seleccionados. Se desarrolló un estudio ecológico, exploratorio, de grupos múltiples, comparando los nueve municipios de la provincia Santiago de Cuba según variables del nivel global, supuestamente relacionadas con la cantidad de casos con covid-19 confirmados desde el 15 de octubre de 2020 hasta el 16 de enero de 2021. Se aplicó el análisis de regresión lineal múltiple para seleccionar el modelo que describiera mejor el comportamiento de los datos y el análisis de clúster para visualizar la agrupación de los municipios. Se evidenció una correlación significativa entre la cantidad de casos con covid-19, la densidad de población y el grado de urbanización. En cambio, en el modelo de regresión solo resultó significativa la densidad poblacional cuando se consideraron los nueve municipios y el índice de masculinidad, cuando se excluyó el municipio atípico, Santiago de Cuba. El índice de masculinidad resultó ser una variable espuria condicionada por la densidad poblacional como variable confusora. El análisis de clúster reveló la formación de tres grupos de municipios, quedando Santiago de Cuba aislado del resto de los municipios.

Some researchers have used demographic indicators to estimate the number of people infected by COVID-19. The first goal of this study is to determine to what extent the incidence of cases of COVID-19 in the municipalities of the province of Santiago de Cuba can be explained by certain demographic indicators. The second goal is to construct a hierarchy of groups of municipalities according to the differentiated behavior of the selected demographic indicators. An ecological, exploratory, multi-group study was developed, comparing the nine municipalities of Santiago de Cuba province according to global level variables, supposedly related to the number of cases with COVID-19 confirmed from October 15, 2020 to January 16, 2021. Multiple linear regression analysis was applied to select the model that best described the behavior of the data and cluster analysis to visualize the grouping of the municipalities. A significant correlation was found between the number of cases with COVID-19, population density and urbanization level. On the other hand, in the regression model, only population density was significant when the nine municipalities were considered and the masculinity index, when the atypical municipality, Santiago de Cuba, was excluded. The masculinity index turned out to be a spurious variable conditioned by population density as a confounding variable. The cluster analysis revealed the formation of three groups of municipalities, with Santiago de Cuba being isolated from the rest of the municipalities.

Indicadores demográficos têm sido usados por alguns pesquisadores para estimar o número de pessoas infectadas pela Covid-19. O primeiro objetivo deste estudo é determinar até que ponto a incidência de casos de Covid-19 nos municípios da província de Santiago de Cuba pode ser explicada por certos indicadores demográficos. O segundo objetivo é construir uma hierarquia de grupos de municípios de acordo com o comportamento diferenciado dos indicadores demográficos selecionados. Foi desenvolvido um estudo ecológico, exploratório e multigrupo, comparando os nove municípios da província de Santiago de Cuba de acordo com variáveis de nível global, supostamente relacionadas ao número de casos de Covid-19 confirmados entre 15 de outubro de 2020 e 16 de janeiro de 2021. A análise de regressão linear múltipla foi aplicada para selecionar o modelo que melhor descrevia o comportamento dos dados e a análise de agrupamento para visualizar o agrupamento dos municípios. Foi encontrada uma correlação significativa entre o número de casos de Covid-19, a densidade populacional e o nível de urbanização. Por outro lado, no modelo de regressão, apenas a densidade populacional era significativa quando os nove municípios foram considerados e o índice de masculinidade, quando o município atípico, Santiago de Cuba, foi excluído. O índice de masculinidade revelou-se uma variável espúria condicionada pela densidade populacional como uma variável confusa. A análise de agrupamento revelou a formação de três grupos de municípios, com Santiago de Cuba sendo isolado do resto dos municípios.

Entrada de viajeros y densidad poblacional en la propagación de la COVID-19 en Cuba / Arrival of travelers and population density in the spread of COVID-19 in Cuba

Introducción: El comportamiento no homogéneo de la cantidad de casos confirmados con COVID-19 en diferentes regiones de Cuba aún no se ha esclarecido, lo cual resultaría de utilidad para la toma de decisiones en futuras epidemias en el país. Objetivo: Determinar la influencia de la entrada de viajeros y la densidad poblacional sobre la distribución no homogénea de la cantidad de casos con COVID-19 por provincias en Cuba. Métodos: Se desarrolló un estudio ecológico, exploratorio, de grupos múltiples, comparando las provincias cubanas según variables del nivel global y agregado, relacionadas con la cantidad de casos con COVID-19, confirmados durante la epidemia en Cuba. Se aplicó el análisis de regresión lineal múltiple para seleccionar el modelo que mejor describe el comportamiento de los datos y el análisis de clúster para visualizar la agrupación de las provincias. Resultados: Se evidenció una correlación significativa entre la cantidad de casos con COVID-19 y la cantidad de viajeros con COVID-19, la cantidad total de viajeros que arribaron al país en marzo y los eventos de trasmisión. En el modelo de regresión resultaron significativas la densidad poblacional y las cantidades de viajeros total y con COVID-19. El análisis de clúster reveló la formación de cuatro grupos de provincias. Conclusiones: La cantidad de casos con COVID-19 por provincia se relaciona con la cantidad de viajeros que entraron al país, con y sin COVID-19, y la densidad poblacional. Se forman cuatro grupos de provincias por su similitud en los aspectos identificados en la regresión(AU)

Introduction: The non-homogeneous behavior of the number of COVID-19 confirmed cases in different regions of Cuba has not yet been clarified, which would be useful for decision-making in future epidemics in the country. Objective: To determine the influence of the arrival of travelers and the population density on the non-homogeneous distribution of the number of COVID-19 cases by provinces in Cuba. Methods: An ecological, exploratory, multiple group study was carried out, comparing Cuban provinces according to variables of the global and aggregate levels, related to the number of COVID-19 cases, confirmed during the epidemic in Cuba. Multiple linear regression analysis was applied to select the model that best describes the behavior of the data and cluster analysis to visualize the grouping of the provinces. Results: A significant correlation was proved between the number of COVID-19 cases and the number of travelers with COVID-19, the total number of travelers who arrived in Cuba in March, and transmission events. In the regression model, the population density and the total number of travelers and those with COVID-19 were significant. The cluster analysis revealed the formation of four groups of provinces. Conclusions: The number of cases with COVID-19 by province is related to the number of travelers who arrived in the country, with and with no COVID-19, and the population density. Four groups of provinces are formed by their similarity in the aspects identified at regression(AU)

Multivariate analysis of correlates of children nutritional status in Harar region, Ethiopia

Background:Malnutrition is defined as deficiencies, excesses or imbalances in a person’s intake of energy and/or nutrients. In Ethiopia malnutrition is one of the most serious health and welfare problems among infants and young children. Malnutrition among children under five years of age is a chronic problem in most regions of Ethiopia, including the Harari region. The main objective of this study was to assess risk factors attributed to nutritional status of children in Harari region.Methods:Data was obtained from Ethiopian Demographic Health Survey, 2016. Different factors were considered as determinants of nutritional status of a child. The study used Multivariate Multiple Linear Regression model to identify significant correlates of children nutritional status.Results:The descriptive statistics in the study revealed that out of a total of 233 children included in the study 21% are underweighted, 19.3% are stunted and 11.2% are wasted in the study area. From Multivariate multiple linear regression, breast feeding factors, health status of child and child vaccination status significantly affect nutritional status of the under five children.Conclusions:The factor analyses conducted in this study indicated that only two factors (instead of 5 original observed variables or items) were sufficient to explain 78.605% of the total variation in PCFA of observed items related to child nutritional status. Factors duration of breast feeding, birth order of a child, current age of child is statistically significant in affecting child malnutrition

Application of a multiple linear regression model of FEV1 in pulmonary function test / 南方医科大学学报

OBJECTIVE@#To construct a multiple linear regression model of forced expiratory volume in 1 second (FEV1) for estimating FEV1 in special populations unable to receive or uncooperative in pulmonary ventilation function tests.@*METHODS@#The multiple linear regression model of FEV1 was constructed based on the data of 813 individuals undergoing pulmonary function tests in First Affiliated Hospital of Zhejiang Chinese Medical University between September, 2017 and September, 2019, and was validated using the data of another 94 individuals from the same hospital between January and July, 2020. FEV1 of the individuals was measured by pulmonary ventilation function test, and respiratory resistance (Rrs) was measured using forced oscillation technique (FOT). Pearson correlation analysis was used to assess the correlation between the factors, and the model equation was established by multiple stepwise regression analysis. The calculated FEV1 based on the model was compared with the measured FEV1 among both the individuals included for modeling and validation.@*RESULTS@#FEV1 was not significantly correlated with BMI (@*CONCLUSIONS@#The multiple linear regression model for calculating FEV1 constructed in this study is suitable for clinical application.

Avaliação de indicadores aplicados a sistemas de abastecimento de água de pequeno porte / Evaluation of indicators applied to small water supply systems

RESUMO O presente trabalho visou avaliar, por meio de regressão linear múltipla (RLM), os indicadores que melhor exprimem a realidade dos sistemas de abastecimento de água de municípios de pequeno porte, com base nos desempenhos financeiro, operacional e de qualidade da água como variáveis de resposta. A organização e a seleção dos dados fiaram-se no Sistema Nacional de Informações sobre Saneamento de 2014, tendo sido selecionados os referentes a 182 municípios de Minas Gerais com população inferior a 10 mil habitantes e 56 indicadores como variáveis explicativas. Por meio da RLM, verificou-se que o comprometimento das receitas com a despesa (margem da despesa de exploração) e a razão entre a arrecadação e as despesas (índice de suficiência de caixa) são as variáveis mais relevantes para descrever o desempenho financeiro. Para descrição do desempenho operacional, os índices de perdas por ligação e de faturamento de água foram os mais recorrentes. Por fim, no que tange ao desempenho de qualidade da água, os modelos apresentaram baixos coeficientes de determinação e a não aderência dos resíduos à distribuição normal.

ABSTRACT The aim of this study was to evaluate, by means of multiple linear regression (MLR), the indicators that best express the reality of small municipalities water supply systems, based on financial, operational and water quality performance as response variables. The organization and selection of the sample were based on the information available in the National Sanitation Information System of 2014, selecting 182 municipalities in Minas Gerais with population below 10,000 inhabitants and 56 indicators as explanatory variables. Through MLR, it was found that the commitment of revenue to expenditure (operating expense margin) and the ratio of collection to expenses (monetary sufficiency index) are the most relevant variables to describe financial performance. For the description of operational performance, the water loss per connection and water billing rates were the most recurrent. Finally, with regard to the performance of water quality, the models presented low determination coefficients and non-adherence of the residues to normal distribution.

A milestone in prediction of the coronary artery dimensions from the multiple linear regression equation

Bachground /aim Coronary artery imaging is one of the most commonly used diagnostic methods. We aimed to investigate whether there is a correlation between left main coronary artery (LMCA), left anterior descending artery (LAD) and left circumflex artery (LCx) artery dimensions in normal cases and a possibility to express the coronary dimensions by multiple linear equations. Materials and methods Images of coronary angiograms of 925 normal cases selected from 3855 cases made up the study population (515 men and 410 women; age range, 30–75 years). The mean age of the patients was 55.50 ± 6.49 years. The mean body mass index was 24.79 ± 1.45 kg/m2 (range, 31.30–21.26 kg/m2). The mean dimensions of LMCA, LAD and LCx were 4.18 ± 0.65 mm, 3.22 ± 0.63 mm and 3.07 ± 0.65 mm, respectively. Correlation between LMCA, LAD and LCx diameters was investigated. Multiple linear regression analysis was used to develop a model to elucidate the relationship between LMCA, LAD and LCx diameters. Results There was a strong correlation between LMCA dimensions and LAD and LCx dimensions (r = 0.526**, p < 0.001* and r = 0.469**, p < 0.001*, respectively). The positive correlation indicated that a regression analysis can be carried out by incorporating the measurements. Coronary artery dimensions were gender specific. Conclusion The present study explored the possibility of explaining the relationship with the LMCA and its branches by multiple linear equations, which may then be used to estimate the reference diameter of a stenosed coronary artery when the other two arteries are normal.

Literature Analysis of Study on Optimal Sampling Points of Clinical Pharmacokinetics Based on Maximum a Posteriori Bayesian Method / 中国药房

OBJECTIVE:To provide reference for the study of optimal sampling points in clinical pharmacokinetics.METHODS:The literatures about optimal sampling points in clinical pharmacokinetics were searched from CNKI,Wanfang database,VIP,PubMed,Medline,ScienceDirect and other databases during Jan.2011-Jun.2016 using "Bayesian estimate(s)""Bayesian estimator(s) Bayesian analysis" "Limited" "Optimal" "Sparse" "Minimal sampling" as retrieval words.The systematic analysis and evaluation were conducted.RESULTS:A total of 1 Chinese literature and 13 English literatures were involved respectively.The drugs they focused on were mainly immunosuppressive agents,antiviral drugs,antibiotics,pediatric individualized medication,etc.Multiple linear regression (MLR) was still the most widely used method in China,while maximum a posteriori Bayesian (MAPB) method was more popular in foreign studies.MLR equation was simple and easy to use,but the sampling was very strict.MAPB method could be completed with less sampling points and sampling time;it was more suitable for clinical practice,but needed professional software.The precision and accuracy of the two methods were similar.The research methods of optimal sampling strategy were quite different but all included 4 steps as prior information ganining,reference value determination,sampling point optimization,prediction capability verification.CONCLUSIONS:MAPB method requires less sampling points and it results are relatively accurate and reliable.It is more suitable for clinical practice and optimal sampling study of clinical pharmacokinetics.

Influential factors for hospitalization costs regarding the final phase of malignant tumor patients in Shanghai / 中南大学学报(医学版)

Objective:To explore the influential factors for hospitalization costs regarding the final phase of malignant tumor patients in Shanghai,and to explore the relevant policy for reasonable control of hospitalization costs.Methods:A total of 10 065 patients with malignant tumors were enrolled in this study.The multiple linear regression analysis was used to seek the determinants for hospitalization cost of malignant tumor patients during the final phase.Results:The median length of hospital stay was 43 days for the patients,with an average age of (70.73±12.87) years.Among them 61.66％ of hospitalized patients were male and the median hospitalization cost of malignancy was 55 447.84 yuan.Hospitalization cost showed the linear regression relationship with type of health care,hospital level,hospital types,tumor types,length of hospital stay,surgery,age,gender,and time from hospital admission to death.Conclusion:Proximity to death in malignant tumor patients is an important factor for the hospitalization cost.Medical resources should be allocated rationally,and the comprehensive measures should be taken to control the cost reasonably.

Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data

ABSTRACT Basal area (BA) is a good predictor of timber stand volume and forest growth. This study developed predictive models using field and airborne LiDAR (Light Detection and Ranging) data for estimation of basal area in Pinus taeda plantation in south Brazil. In the field, BA was collected from conventional forest inventory plots. Multiple linear regression models for predicting BA from LiDAR-derived metrics were developed and evaluated for predictive power and parsimony. The best model to predict BA from a family of six models was selected based on corrected Akaike Information Criterion (AICc) and assessed by the adjusted coefficient of determination (adj. R²) and root mean square error (RMSE). The best model revealed an adj. R²=0.93 and RMSE=7.74%. Leave one out cross-validation of the best regression model was also computed, and revealed an adj. R² and RMSE of 0.92 and 8.31%, respectively. This study showed that LiDAR-derived metrics can be used to predict BA in Pinus taeda plantations in south Brazil with high precision. We conclude that there is good potential to monitor growth in this type of plantations using airborne LiDAR. We hope that the promising results for BA modeling presented herein will stimulate to operate this technology in Brazil.

Investigation on the sub-health status of migrant workers in Dongguan City / 重庆医学

Objective To investigate the prevalence and influencing factors of sub-health status of the migrant workers in Dongguan City,in order to provide scientific preferences for preventing sub-health status.Methods Using the stratified random sampling method,740 migrant workers from ten towns(disetricts) in Dongguan city from August 2015 to August 2016 were recruited in this study.The sub-health measurement scale version 1.0 (SHMS V1.0) was applied to evaluate the sub-health status of migrant workers.The SHMS V1.0 scores were compared among migrant workers with different demographic characteristics,and the multivariate linear regression analysis was utilized to explore the influencing factors.Results A total of 718 valid questionnaires were collected,and the effective recovery rate was 97.03％.The sub-health status was detected in 483 migrant workers,and the prevalence rate of sub-health status was 81.6％.The migrant workers' subscale scores of physical sub-health (PS),mental subhealth (MS),social sub-health (SS) and total scale (TS) were (70.25-4-12.25),(64.21± 13.83),(62.21-4-13.87) and (66.114-11.15),respectively.The PS scale scores among migrant workers with different monthly household incomes per capita,and different inhabit situations;the MS scale scores among migrant workers with different ages,educations,marital status,monthly household incomes per capita,and inhabit situations;the SS scale scores among migrant workers with different genders,educations,and inhabit situations;and TS scores mong migrant workers with different educations,monthly household incomes per capita,and inhabit situations were statistically significant different (P＜0.05).The multivariate linear regression analysis showed that educations and inhabit situations were the influencing factors for TS score (P＜0.05).Conclusion The sub-health status of migrant workers in Dongguan City is serious,and the influencing factors are educations and inhabit situations.

An adaptive algorithm for single-trial P300 evoked potential extraction based on dynamic feature library / 国际生物医学工程杂志

Objective The single-trial extraction method of evoked potential has been one of the problems in EEG information processing field.According to the characteristics of somatosensory evoked electroencephalogram (EEG) with low signal-to-noise ratio and large parameter variation between trials,a novel single-trial extraction method for evoked potentials was proposed.This method aims to further improve the accuracy and characteristics of the single-trial extraction algorithm,preserve more dynamic characteristics between trials,and improve the estimation accuracy.Methods Based on wavelet filtering and multiple linear analysis,a new single-trial extraction method for EEG P300 parameters was proposed by applying the adaptive dynamic feature library.Four groups of wavelet filtered evoked EEG data were randomly selected,and used to build the feature library using overlapping average method and principal component analysis.For the single-trial extracted EEG data,the component with the highest correlation coefficient related with the current data was selected as the independent variable from the feature library,and the relevant multiple linear regression analysis was conducted.The single-trial evoked potential signal was reconstructed by the regression analysis results,in which the key features such as latency and amplitude were automatically extracted.Results Compared with the benchmark values determined by experts,the proposed algorithn can obtain more accurate estimation values of latency and amplitude in P300 components.The average difference of latency and amplitude by the proposed algorithm is (11.16±8.60) ms and (1.40±1.34)μV,respectively.These two values obtained by the proposed algorithm are much closer to that obtained by the commonly used overlapping average method of (23.26±25.76) ms and (2.52±2.50) μV,respectively.These results show that the proposed algorithm has significant advantages comparing with the traditional multiple linear regression analysis algorithm.Conclusions The dynamic updating principal component sample library of EEG data was applied to wavelet filtering and multiple linear regression,thus the dynamic characteristics were effectively preserved,and the accuracy of parameter estimation was improved.

Investigation on the sub-health status of migrant workers in Dongguan City / 重庆医学

Objective To investigate the prevalence and influencing factors of sub-health status of the migrant workers in Dongguan City,in order to provide scientific preferences for preventing sub-health status.Methods Using the stratified random sampling method,740 migrant workers from ten towns(disetricts) in Dongguan city from August 2015 to August 2016 were recruited in this study.The sub-health measurement scale version 1.0 (SHMS V1.0) was applied to evaluate the sub-health status of migrant workers.The SHMS V1.0 scores were compared among migrant workers with different demographic characteristics,and the multivariate linear regression analysis was utilized to explore the influencing factors.Results A total of 718 valid questionnaires were collected,and the effective recovery rate was 97.03％.The sub-health status was detected in 483 migrant workers,and the prevalence rate of sub-health status was 81.6％.The migrant workers' subscale scores of physical sub-health (PS),mental subhealth (MS),social sub-health (SS) and total scale (TS) were (70.25-4-12.25),(64.21± 13.83),(62.21-4-13.87) and (66.114-11.15),respectively.The PS scale scores among migrant workers with different monthly household incomes per capita,and different inhabit situations;the MS scale scores among migrant workers with different ages,educations,marital status,monthly household incomes per capita,and inhabit situations;the SS scale scores among migrant workers with different genders,educations,and inhabit situations;and TS scores mong migrant workers with different educations,monthly household incomes per capita,and inhabit situations were statistically significant different (P＜0.05).The multivariate linear regression analysis showed that educations and inhabit situations were the influencing factors for TS score (P＜0.05).Conclusion The sub-health status of migrant workers in Dongguan City is serious,and the influencing factors are educations and inhabit situations.

An adaptive algorithm for single-trial P300 evoked potential extraction based on dynamic feature library / 国际生物医学工程杂志

Objective The single-trial extraction method of evoked potential has been one of the problems in EEG information processing field.According to the characteristics of somatosensory evoked electroencephalogram (EEG) with low signal-to-noise ratio and large parameter variation between trials,a novel single-trial extraction method for evoked potentials was proposed.This method aims to further improve the accuracy and characteristics of the single-trial extraction algorithm,preserve more dynamic characteristics between trials,and improve the estimation accuracy.Methods Based on wavelet filtering and multiple linear analysis,a new single-trial extraction method for EEG P300 parameters was proposed by applying the adaptive dynamic feature library.Four groups of wavelet filtered evoked EEG data were randomly selected,and used to build the feature library using overlapping average method and principal component analysis.For the single-trial extracted EEG data,the component with the highest correlation coefficient related with the current data was selected as the independent variable from the feature library,and the relevant multiple linear regression analysis was conducted.The single-trial evoked potential signal was reconstructed by the regression analysis results,in which the key features such as latency and amplitude were automatically extracted.Results Compared with the benchmark values determined by experts,the proposed algorithn can obtain more accurate estimation values of latency and amplitude in P300 components.The average difference of latency and amplitude by the proposed algorithm is (11.16±8.60) ms and (1.40±1.34)μV,respectively.These two values obtained by the proposed algorithm are much closer to that obtained by the commonly used overlapping average method of (23.26±25.76) ms and (2.52±2.50) μV,respectively.These results show that the proposed algorithm has significant advantages comparing with the traditional multiple linear regression analysis algorithm.Conclusions The dynamic updating principal component sample library of EEG data was applied to wavelet filtering and multiple linear regression,thus the dynamic characteristics were effectively preserved,and the accuracy of parameter estimation was improved.

Study of critical thinking status and its influence factors of medical students / 中华医学教育探索杂志

Objective To understand the status and related influencing factors of critical thinking of medical students, so as to provide recommendations for the training methods of critical thinking of medical students. Methods A total of 380 medical students in third and fourth grade of a medical university were investigated by using the California Critical Thinking Tendency Questionnaire and stratified random sam-pling method. 356 valid questionnaires were recovered. SPSS 22.0 software was used for statistical analysis, including t-test, analysis of variance and multiple linear regression analysis and other methods. Results The Cronbach'sαcoefficient of the scale was 0.728, and the cumulative variance contribution rate was 68.43%. The average score of positive critical thinking ability of medical students was 196.02, showing that they had positive thinking ability. Multiple linear regression analysis showed that the degree of extracurricular read-ing, whether there was a part-time job, and the students' self-study time per week had positive correlation with the critical thinking of medical students (P<0.05). Conclusion The critical thinking ability of medical students needs to be improved and the factors of critical thinking need to be further studied, and the research and practice of cultivating medical students' critical thinking ability must be deepened.

Risk factors for the complications of primary hypospadias urethroplasty with the urethral plate reserved / 中华男科学杂志

Objective@#To investigate the risk factors for the complications of urethroplasty in patients with primary hypospadias by postoperative follow-up observation.@*METHODS@#We retrospectively analyzed 110 cases of primary hypospadias repair performed from November 2010 to October 2015, including 70 cases of tubularized incised plate (TIP) urethroplasty and 40 cases of inlay internal preputial graft (IIPG) urethroplasty, all with the urethral plate reserved. We followed up the patients for 15.6－36 months, (27.3 ± 0.52) mo for those with and (26.9 ± 0.22) mo for those without complications. The mean age of the two groups of patients was (7.5 ± 0.2) and (7.0 ± 0.5) yr, respectively.@*RESULTS@#The follow-up data were collected from all the patients, 17 (15.5%) with and 93 (84.5%) without complications. The success rate of surgery was 84.5%. There were no statistically significant differences in the follow-up time and age between the two groups of patients (P >0.05). Single-factor analysis of variance showed significant differences between the complication and non-complication groups in the preoperative urethral opening (P <0.01), ventral penile curvature (P <0.01), and length of urethral defect (P = 0.04), while multiple linear regression analysis exhibited that only ventral curvature was associated with the postoperative complications of the patients (OR = 1.12, 95% CI: 1.06－1.19, P<0.01).@*CONCLUSIONS@#We chose single-stage urethroplasty with the urethral plate reserved for the treatment of primary hypospadias and achieved satisfactory outcomes. Ventral penile curvature is an independent risk factor for the complications of primary hypospadias, and a higher degree of curvature is associated with a higher incidnece of complications.